Multimorbidity and Antiretroviral Therapy Failure: Dose-Response Relationship in a Large Indian Public-Sector Cohort
DOI:
https://doi.org/10.55489/njcm.170120266013Keywords:
HIV/AIDS, Antiretroviral Therapy (ART), Multimorbidity, Adherence, HIV Treatment OutcomesAbstract
Background: The increasing burden of multimorbidity among people living with HIV (PLHIV) threatens the effectiveness of antiretroviral therapy (ART). Very few studies have been published on the predictors of treatment outcome with concurrent medical conditions in resource-poor setting. In this study; we assessed the relationship of multimorbidity with ART failure among HIV-infected adults in Karnataka, India.
Methods: A retrospective study of 2,709 HIV-positive adults started on antiretroviral therapy (ART) from 2019-2023 in a district treatment center in Belgaum. Data gathered were demographic, clinical data like comorbidities (tuberculosis, diabetes and hepatitis B/C) and treatment outcomes. Multivariate logistic regression found independent predictors of treatment failure according to virological, immunological, or clinical definitions.
Results: In total, 542 (19.54%) participants failed treatment. A strong dose-response pattern was observed in treatment failure: 16.3% among patients without comorbidities increased to 22.0% with one co-morbidity condition, 51.6% with two, and 52.0% with all three comorbidities. Tuberculosis showed the highest independent risk (adjusted OR 1.88; 95% CI: 1.44-2.45), and individuals with ≥2 comorbidities had more than double the odds of failure (adjusted OR 2.54; 95% CI: 1.88-3.42). Hepatitis B/C coinfection was also strongly associated with failure (adjusted OR 1.67; 95% CI: 1.11-2.52). Increasing multimorbidity corresponded with poorer CD4 recovery, lower viral suppression, and reduced adherence.
Conclusions: Multimorbidity undermines ART efficacy with a combined impact on immune recovery and treatment adherence. These data support the design of integrated care models for HIV and comorbidities in settings with high burdens. Resource allocation and risk stratification based on the comorbidity burden might be optimized and improve treatment outcomes.
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Copyright (c) 2025 Nikhil P Hawal, Padmaja R Walavekar, Sonam Bhandurge

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